Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Put your homework on GitHub!

If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 4th weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab under Stage and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

world <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "terrain",
    zoom = 2)

ggmap(world) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = `Ownership Type`), 
             alpha = .3, 
             size = .1) +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Starbucks Locations and Ownership Types Around the World")

Interpretation: This map of Starbucks locations by Ownership type shows that there are some areas of the world where specific Ownership types are much more common. Some parts of the world (like Japan, Eastern Europe, Korea, and India) have a lot more joint ventures. This probably has something to do with laws about what types of franchises you can run, or are easier to run, in different countries.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

#I'm not exactly sure what the 5 county metro area is, so I made my best guess
Twin_cities <- get_stamenmap(
    bbox = c(left = -93.7, bottom = 44.7, right = -92.7, top = 45.3), 
    maptype = "terrain",
    zoom = 10)

ggmap(Twin_cities) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = `Ownership Type`), 
             alpha = .5, 
             size = 1) +
  theme_map() +
  theme(legend.position = "right") +
  labs(title = "Starbucks Locations and Ownership Type in the Twin Cities Metro Area")

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

Description: Increasing the zoom adds a lot more detail on streets, names of places, and geography, but it takes much longer to load. Decreasing the zoom makes it blurry and not as detailed.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

Twin_cities <- get_stamenmap(
    bbox = c(left = -93.7, bottom = 44.7, right = -92.7, top = 45.3), 
    maptype = "toner",
    zoom = 10)

ggmap(Twin_cities) +
  labs(title = "Toner Style Map of the Twin Cities", y = "Latitude", x = "Longitude")

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

Twin_cities <- get_stamenmap(
    bbox = c(left = -93.7, bottom = 44.7, right = -92.7, top = 45.3), 
    maptype = "terrain",
    zoom = 10)

ggmap(Twin_cities)+
  annotate("point", x=-93.17123, y=44.93790, color = "red") +
  annotate("text", x=-93.17123, y=44.92790, color = "black",
           size=2, label = "Macalester College") +
  labs(title = "Location of Macalester College in the Twin Cities", y = "Latitude", x = "Longitude")

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does.

Review: First you read in the data from dropbox and assigned it to the name census_pop_est_2018. Then you separated the state variable to get isolate the “.” in front of each name, selected for everything except the “.”, and then mutated the state variable to make the state names all lowercase. Then you left joined this modified census_pop_est_2018 to the Starbucks data by state (the names of the state variables were different, so you had to specify that state_name = state). Finally you added a column with the number of starbucks in each state divided by the state population times 10000. This all got saved to a new table named starbucks_with_2018_pop_est.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

states_map <- map_data("state")

starbucks_with_2018_pop_est %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
  geom_point(data = Starbucks %>% filter(Country == "US" & `State/Province` !="AK" & `State/Province` !="HI"),
             aes(x = Longitude, y = Latitude),
             size = .05,
             alpha = .2, 
             color = "goldenrod") +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "Starbucks Locations and State Proportions of Starbucks per 10,000 Citizens",
       caption = "Plot by Bea Green",
       fill = "Starbucks per 10,000 Citizens") +
  scale_fill_viridis_c(option= "mako") +
  theme_map() +
  theme(legend.background = element_blank(),
        legend.position = "bottom")

Conclusions: From this map I conclude that while the yellow markers showing Starbucks locations make it appear as if there are a lot of Starbucks on the east coast, compared to the population of those states there are many more Starbucks per person on the west coast. Unsurprisingly, the highest Starbucks to person ratio in the US is in Washington state, where Starbucks originated.

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

bea_favorite_plcs <- tibble(
  place = c("Home", "Mill Garden Store", "Samurai Restaurant", "Ice Cream Stand",
            "Lake Morey", "Friend's House", "Farm Way Store", "Indian Pond",
            "Free Dirt", "Stargazing Field"),
  long = c(-72.131676, -72.156368, -72.146685, -72.149484, 
           -72.159692, -72.230463, -72.116286, -72.054637,
           -72.124005, -72.122385),
  lat = c(43.896003, 43.871968, 43.905040, 43.903950,
          43.921137, 44.032146, 43.980569, 43.930951,
          43.899657, 43.893588),
  top_three = c("Top 3", "other", "other", "other",
                "Top 3", "Top 3", "other", "other",
                "other", "other"))
  
bea_pal <- colorFactor(palette = c("#4B0C88", "#9F63D9"), 
               levels = c("Top 3", "other"))

leaflet(bea_favorite_plcs) %>% 
  addProviderTiles(providers$Esri.WorldTopoMap) %>% 
  addPolylines(lng = ~long, 
            lat = ~lat, 
             color = "#E9BC31") %>% 
  addCircles(radius = 10,
             color = ~bea_pal(top_three),
             opacity = 1,
             label = ~paste(str_to_title(place))) %>% 
  addLegend("topright",
           pal = bea_pal,
           values= ~top_three,
           opacity = 1,
           title = "Bea's Top Locations in the Upper Valley")

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

Wash_DC <- get_stamenmap(
    bbox = c(left = -77.24, bottom = 38.79, right = -76.9, top = 39.13), 
    maptype = "toner-lite",
    zoom = 12)

dept_loc<- Stations %>% 
  summarize(name, lat, long) %>% 
  left_join(Trips %>% summarize(sdate, sstation),
            by = c("name" = "sstation")) %>% 
  group_by(name) %>% 
  summarize(departures = n(), lat, long) %>% 
  distinct() 

ggmap(Wash_DC) + 
  geom_point(data = dept_loc, 
             aes(x = long, y = lat, color = departures), 
             alpha = .7, 
             size = 1) +
  theme_map() +
  scale_color_gradient(low ="royalblue3", high = "firebrick1") +
  theme(legend.position = "left",
        plot.title.position = "plot") +
  labs(title = "Departures from Bike Rental Stations in DC ")

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

cas_sta<- Stations %>% 
  summarize(name, lat, long) %>% 
  left_join(Trips %>% summarize(sdate, sstation, client),
            by = c("name" = "sstation")) %>% 
  group_by(name) %>% 
  summarize(perc_casual = sum(ifelse(client == "Casual", 1, 0))/n()*100,
            lat, long) %>%
  distinct()

ggmap(Wash_DC) + 
  geom_point(data = cas_sta, 
             aes(x = long, y = lat, color = perc_casual), 
             alpha = .7, 
             size = 1) +
  theme_map() +
  scale_color_gradient(low ="royalblue3", high = "firebrick1") +
  theme(legend.position = "right",
        plot.title.position = "plot") +
  labs(title = "Percentage of Casual Users at Bike Rental Departure Stations in DC",
       color = "Percent of Casual Users")

Description: In this map of bike departures you can see that areas with a higher percentage of casual users are in the downtown area, probably where all of the tourist attractions are, and in an area up to the Northeast. The further you get from downtown and by the Potomac River, the fewer casual users there are.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

current_cases<- covid19 %>%
  group_by(state, fips) %>% 
  top_n(n = 1, wt = date) %>% 
  mutate(state = str_to_lower(state))

ggplot(current_cases) +
  geom_map(map = states_map,
           aes(map_id = state, fill = cases)) +
  expand_limits(x = states_map$long, y = states_map$lat) +
  labs(title = "Current Cumulative Covid19 Cases in Each State") +
  theme_map()

Description: This map showing the current cumulative cases of Covid19 in each state is essentially just a population map of the United States because by counting the raw numbers, states with higher populations automatically will have higher numbers of cases. California, Texas, New York, and Florida all rank high, which are the most populous states in the country. This doesn’t help us compare how the states dealt with Covid19 and what affect their different strategies had on cumulative case count.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

current_cases_by_pop <-
  current_cases %>% 
  left_join(census_pop_est_2018,
            by = "state") %>% 
  mutate(cases_per_10000 = (cases/est_pop_2018)*10000)

ggplot(current_cases_by_pop) +
  geom_map(map = states_map,
           aes(map_id = state, fill = cases_per_10000)) +
  expand_limits(x = states_map$long, y = states_map$lat) +
  labs(title = "Cumulative Covid19 Cases per 10,000 People in Each State",
       fill = "Cases per 10,000 People") +
  theme_map()

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>% 
  group_by(neighborhood) %>% 
  summarize(stops = n(), perc_sus = sum(ifelse(problem == "suspicious", 1, 0))/n()*100) %>% 
  arrange(desc(stops))

print(mpls_suspicious)

## # A tibble: 87 x 3
##    neighborhood    stops perc_sus
##    <fct>           <int>    <dbl>
##  1 Downtown West    4409     75.7
##  2 Whittier         3328     40.6
##  3 Near - North     2256     41.5
##  4 Lyndale          2154     40.5
##  5 Jordan           2075     39.2
##  6 Hawthorne        2031     38.7
##  7 Marcy Holmes     1798     28.8
##  8 Lowry Hill East  1491     40.4
##  9 East Phillips    1387     69.1
## 10 Folwell          1230     39.4
## # … with 77 more rows

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

sus_stops_pal = colorFactor(palette = c("#FD4922", "#2271FD"), 
               levels = c("suspicious", "traffic"))

leaflet(MplsStops) %>% 
  addProviderTiles(providers$Stamen.Terrain) %>% 
  addCircleMarkers(lng = ~long,
                   lat = ~lat,
                   stroke = FALSE,
                   radius = 1,
                   color = ~sus_stops_pal(problem),
                   opacity = .5) %>% 
  addLegend(position = "bottomright",
            pal = sus_stops_pal, 
            values = ~problem,
            title = "2017 Police Stops in <br>Minneapolis by Problem Type")

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)

mpls_all <- mpls_nbhd %>% 
  left_join(mpls_suspicious,
            by = c("BDNAME" = "neighborhood")) %>% 
  left_join(MplsDemo,
            by = c("BDNAME" = "neighborhood"))

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

mpls_pal <- colorNumeric("inferno", 
                     domain = mpls_all$perc_sus)

leaflet(mpls_all) %>% 
  addTiles() %>% 
  addPolygons(fillColor = ~mpls_pal(perc_sus),
              stroke = FALSE,
              fillOpacity = .7,
              label = ~paste(BDNAME)) %>% 
  addLegend(position = "bottomleft",
            pal = mpls_pal, 
            values = ~perc_sus,
            title = "2017 Percentage of Minneapolis<br>Police Stops for 'Suspicious Vehicle<br>or Person' by Neighborhood")

Description: This map showing the percentage of Police stops that were due to a “suspicious vehicle or person” in each Minneapolis neighborhood shows that whether the Police were stopping people due to traffic or suspicion has a lot to do with geography (and presumably social factors that underlie geography). There is a much higher percentage of suspicious stops in the Southeastern neighborhoods, for example over 90% of stops in Morris Park were due to suspicion. On the other hand, areas with lower suspicion stops (and therefore higher traffic stops) include generally the Northeastern part of the city.

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

Question: Are some parts of Minneapolis more heavily policed compared to their population?

mpls_extended <- mpls_all %>% 
  mutate(stops_per_pop = stops/population)

mpls_pal4 <- colorNumeric("inferno", 
                     domain = mpls_extended$stops_per_pop)

leaflet(mpls_extended) %>% 
  addTiles() %>% 
  addPolygons(fillColor = ~mpls_pal4(stops_per_pop),
              stroke = FALSE,
              fillOpacity = .7,
              label = ~paste(BDNAME)) %>% 
  addLegend(position = "bottomleft",
            pal = mpls_pal4, 
            values = ~stops_per_pop,
            title = "2017 Minneapolis Police Stops <br> per Neighborhood Population")

Description: This map was really surprising! There is significant variation across neighborhoods for the number of stops per population, with the standout case being the Mid-City Industrial neighborhood where there were actually more police stops than there are residents! This raises a lot of questions for me about what is going on there. The higher number of stops per population in the downtown areas make slightly more sense as many people visit there for work or entertainment, but don’t necessarily live there. Other neighborhoods with higher values may indicate some disparity of where police go and where they tend to stop people. Overall I was pretty surprised by just how high these numbers were: my scale shows that in just under half the neighborhoods (40/87), stops per population was at or over 0.1, indicating that at least 1 in 10 residents (or visitors) were stopped by the police in 2017.

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

Bea’s 04 exersizes .md

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?